{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LENGTH = 1400 # 句子 上限1400个词\n",
    "EMBEDDING_DIM = 100 # 100d 词向量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集\n",
    "HTTP DATASET CSIC 2010: http://www.isi.csic.es/dataset/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36000 36000 31020\n"
     ]
    }
   ],
   "source": [
    "# 获取数据集\n",
    "# GET 以 Connection + /n + /n 结尾\n",
    "# POST 以 Content-Length + /n + params + /n 结尾\n",
    "\n",
    "def read_data(path):\n",
    "    data = []\n",
    "    labels = []\n",
    "    with open(path) as f:\n",
    "        while True:\n",
    "            line = f.readline()\n",
    "            content = line\n",
    "            if line == '': break \n",
    "            if line[:3] == 'GET':\n",
    "                while True:\n",
    "                    line = f.readline()\n",
    "                    if line[:10] == 'Connection': break\n",
    "                    else: content += line\n",
    "                f.readline() # none\n",
    "                f.readline() # none\n",
    "            elif line[:4] == 'POST':\n",
    "                while True:\n",
    "                    line = f.readline()\n",
    "                    if line[:14] == 'Content-Length': break\n",
    "                    else: content += line\n",
    "                f.readline() # none\n",
    "                content += f.readline()\n",
    "                f.readline() # none\n",
    "            data.append(content)\n",
    "    return data\n",
    "\n",
    "data1 = 'data/normalTrafficTest.txt'\n",
    "data2 = 'data/normalTrafficTraining.txt'\n",
    "data3 = 'data/anomalousTrafficTest.txt'\n",
    "X1 = read_data(data1)\n",
    "X2 = read_data(data2)\n",
    "X3 = read_data(data3)\n",
    "print(len(X1), len(X2), len(X3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103020 103020\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "labels = []\n",
    "data.extend(X1)\n",
    "labels.extend([1] * len(X1))\n",
    "data.extend(X2)\n",
    "labels.extend([1] * len(X2))\n",
    "data.extend(X3)\n",
    "labels.extend([0] * len(X3))\n",
    "print(len(data), len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GET http://localhost:8080/tienda1/index.jsp HTTP/1.1\\nUser-Agent: Mozilla/5.0 (compatible; Konqueror/3.5; Linux) KHTML/3.5.8 (like Gecko)\\nPragma: no-cache\\nCache-control: no-cache\\nAccept: text/xml,application/xml,application/xhtml+xml,text/html;q=0.9,text/plain;q=0.8,image/png,*/*;q=0.5\\nAccept-Encoding: x-gzip, x-deflate, gzip, deflate\\nAccept-Charset: utf-8, utf-8;q=0.5, *;q=0.5\\nAccept-Language: en\\nHost: localhost:8080\\nCookie: JSESSIONID=EA414B3E327DED6875848530C864BD8F\\n'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'POST http://localhost:8080/tienda1/publico/anadir.jsp HTTP/1.1\\nUser-Agent: Mozilla/5.0 (compatible; Konqueror/3.5; Linux) KHTML/3.5.8 (like Gecko)\\nPragma: no-cache\\nCache-control: no-cache\\nAccept: text/xml,application/xml,application/xhtml+xml,text/html;q=0.9,text/plain;q=0.8,image/png,*/*;q=0.5\\nAccept-Encoding: x-gzip, x-deflate, gzip, deflate\\nAccept-Charset: utf-8, utf-8;q=0.5, *;q=0.5\\nAccept-Language: en\\nHost: localhost:8080\\nCookie: JSESSIONID=788887A0F479749C4CEEA1E268B4A501\\nContent-Type: application/x-www-form-urlencoded\\nConnection: close\\nid=1&nombre=Jam%F3n+Ib%E9rico&precio=39&cantidad=41&B1=A%F1adir+al+carrito\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 80 unique tokens.\n",
      "Shape of data tensor: (103020, 1400)\n"
     ]
    }
   ],
   "source": [
    "# https://keras-cn-docs.readthedocs.io/zh_CN/latest/blog/word_embedding/\n",
    "import numpy as np\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# tokenizer\n",
    "texts = data\n",
    "tokenizer = Tokenizer(char_level=True)\n",
    "tokenizer.fit_on_texts(texts)\n",
    "word_index = tokenizer.word_index\n",
    "\n",
    "# sequences\n",
    "sequences = tokenizer.texts_to_sequences(data)\n",
    "\n",
    "# padding\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "print('Shape of data tensor:', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "token_path = 'model/tokenizer.pkl'\n",
    "pickle.dump(tokenizer, open(token_path, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# 打乱顺序\n",
    "index = [i for i in range(len(data))]\n",
    "random.shuffle(index)\n",
    "data = np.array(data)[index]\n",
    "labels = np.array(labels)[index]\n",
    "\n",
    "TRAIN_SPLIT = 0.8 # 20% 测试集\n",
    "TRAIN_SIZE = int(len(data) * TRAIN_SPLIT)\n",
    "\n",
    "X_train, X_test = data[0:TRAIN_SIZE], data[TRAIN_SIZE:]\n",
    "Y_train, Y_test = labels[0:TRAIN_SIZE], labels[TRAIN_SIZE:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train len: 82416\n",
      "test len: 20604\n"
     ]
    }
   ],
   "source": [
    "print('train len:', len(X_train))\n",
    "print('test len:', len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n",
    "\n",
    "G = 1 # GPU 数量\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)\n",
    "session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True))\n",
    "K.set_session(session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN + Bi-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 1400, 100)         8100      \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 1398, 128)         38528     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 1398, 128)         512       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 1398, 128)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 349, 128)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 128)               98816     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 1)                 4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 154,537\n",
      "Trainable params: 154,151\n",
      "Non-trainable params: 386\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, BatchNormalization, Flatten\n",
    "from keras.layers import Dense, LSTM, Convolution1D, MaxPooling1D\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "\n",
    "QA_EMBED_SIZE = 64\n",
    "DROPOUT_RATE = 0.3\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(len(word_index) + 1, EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH))\n",
    "model.add(Convolution1D(filters=128, kernel_size=3, padding='valid', activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling1D(4))\n",
    "model.add(Bidirectional(LSTM(QA_EMBED_SIZE, return_sequences=False, dropout=DROPOUT_RATE, recurrent_dropout=DROPOUT_RATE)))\n",
    "\n",
    "model.add(Dense(QA_EMBED_SIZE))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型可视化 https://keras-cn.readthedocs.io/en/latest/other/visualization/\n",
    "from keras.utils import plot_model\n",
    "from IPython import display\n",
    "\n",
    "# pip install pydot-ng\n",
    "# sudo apt-get install graphviz\n",
    "plot_model(model, to_file=\"img/model-cnn-blstm.png\", show_shapes=True)\n",
    "display.Image('img/model-cnn-blstm.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 57691 samples, validate on 24725 samples\n",
      "Epoch 1/5\n",
      "57691/57691 [==============================] - 1098s 19ms/step - loss: 0.4462 - acc: 0.8285 - precision: 0.8362 - recall: 0.9460 - f1: 0.8847 - val_loss: 0.1520 - val_acc: 0.9774 - val_precision: 0.9748 - val_recall: 0.9932 - val_f1: 0.9837\n",
      "Epoch 2/5\n",
      "57691/57691 [==============================] - 1096s 19ms/step - loss: 0.1362 - acc: 0.9801 - precision: 0.9793 - recall: 0.9925 - f1: 0.9857 - val_loss: 0.0900 - val_acc: 0.9855 - val_precision: 0.9859 - val_recall: 0.9933 - val_f1: 0.9895\n",
      "Epoch 3/5\n",
      "57691/57691 [==============================] - 1109s 19ms/step - loss: 0.0838 - acc: 0.9853 - precision: 0.9844 - recall: 0.9948 - f1: 0.9895 - val_loss: 0.0587 - val_acc: 0.9893 - val_precision: 0.9877 - val_recall: 0.9970 - val_f1: 0.9922\n",
      "Epoch 4/5\n",
      "57691/57691 [==============================] - 1107s 19ms/step - loss: 0.0602 - acc: 0.9883 - precision: 0.9870 - recall: 0.9964 - f1: 0.9916 - val_loss: 0.0418 - val_acc: 0.9907 - val_precision: 0.9913 - val_recall: 0.9953 - val_f1: 0.9932\n",
      "Epoch 5/5\n",
      "57691/57691 [==============================] - 1097s 19ms/step - loss: 0.0455 - acc: 0.9903 - precision: 0.9893 - recall: 0.9970 - f1: 0.9930 - val_loss: 0.0333 - val_acc: 0.9923 - val_precision: 0.9908 - val_recall: 0.9982 - val_f1: 0.9944\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd97c1bdef0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
    "# from keras.utils import multi_gpu_model\n",
    "from evaluate import *\n",
    "\n",
    "EPOCHS = 5\n",
    "BATCH_SIZE = 64 * G\n",
    "VALIDATION_SPLIT = 0.3 # 30% 验证集\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "model_checkpoint = ModelCheckpoint('model/model-cnn-blstm.h5', save_best_only=True, save_weights_only=True)\n",
    "tensor_board = TensorBoard('log/tflog-cnn-blstm', write_graph=True, write_images=True)\n",
    "\n",
    "# model = multi_gpu_model(model)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy', precision, recall, f1])\n",
    "\n",
    "model.fit(X_train, Y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, \n",
    "          validation_split=VALIDATION_SPLIT, shuffle=True, \n",
    "          callbacks=[early_stopping, model_checkpoint, tensor_board])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20604/20604 [==============================] - 78s 4ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0360177080261779,\n",
       " 0.9910696952511004,\n",
       " 0.9892460173992156,\n",
       " 0.9979366264720955,\n",
       " 0.9934972989006057]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test, verbose=1, batch_size=BATCH_SIZE)"
   ]
  }
 ],
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